Applying a Model

Overview

• Can save a trained model.
• Load it and make predictions for new data.
• Rank on predictions and trade (more later).
• Important to interpret the model
  • What combinations of features does it like/dislike?
  • What industries does it like/dislike?

Repeat prior example

• Use roeq and mom12m for 2021-01 as before.
• Predict rnk as before.
• Use a neural net with two hidden layers: 4 neurons in the first and 2 in the second.

Define, fit and save model

from sklearn.neural_network import MLPRegressor

X = data[["roeq", "mom12m"]]
y = data["rnk"]

model = MLPRegressor(
  hidden_layer_sizes=(4, 2),
  random_state=0
)
model.fit(X,y)

from joblib import dump, load
dump(model, "net-example.joblib")

Example of applying a model

• As an example, treat 2021-02 as new data.
• We’re at the beginning of 2021-02. We know the predictors but not the returns.
• We apply the model to the predictors to predict returns.
• We use the predicted returns to form a portfolio.

Get the 2021-02 data

new = pd.read_sql(
    """
    select ticker, date, ret, roeq, mom12m
    from data
    where date='2021-02'
    """, 
    conn
)
new = new.dropna()

Load and apply the model

model = load("net-example.joblib")

X = new[["roeq", "mom12m"]]

new["prnk"] = model.predict(X)

Order stocks by predictions

new = new.sort_values(
  by="prnk",
  ascending=False
)
new.head(10)